A predictive model for endometrial cancer recurrence based on molecular markers and clinicopathologic parameters: A double-center retrospective study
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Summary
This summary is machine-generated.This study developed a new model to predict endometrial cancer recurrence using molecular markers and patient data. The model accurately identifies high-risk patients, improving prognostic predictions.
Area Of Science
- Oncology
- Molecular Biology
- Biostatistics
Background
- Endometrial cancer (EC) recurrence poses a significant challenge in patient management.
- Accurate prediction of EC recurrence is crucial for personalized treatment strategies.
Purpose Of The Study
- To develop and validate a predictive model for endometrial cancer recurrence.
- To integrate molecular markers and clinicopathologic parameters for enhanced prognostic accuracy.
Main Methods
- Retrospective analysis of 1348 patients from two centers, divided into training (70%) and validation (30%) cohorts.
- Utilized uni- and multivariate Cox regression to identify recurrence predictors.
- Constructed a nomogram for predicting recurrence-free survival (RFS) and validated its accuracy.
Main Results
- Estrogen receptor (ER) and P53 expression were significant predictors of EC recurrence.
- The developed nomogram demonstrated good predictive accuracy for 1-, 3-, and 5-year RFS rates.
- An optimal risk threshold was established, differentiating effectively between high- and low-risk patient groups.
Conclusions
- The novel model, combining molecular indicators and clinicopathologic data, offers superior prediction of EC patient prognosis.
- This integrated approach surpasses traditional prediction models in accuracy.
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